Data Acquisition and Cleaning with Python

Raw data is almost always messy, incomplete, and disorganized. Before you can perform any meaningful analysis or build machine learning models, you must know how to locate, extract, and clean your data. This text-based course guides you through the essential techniques of data acquisition and data cleaning. You will learn how to transition from chaotic, raw data sources to tidy, analysis-ready datasets using modern Python tools and best practices. What you'll learn: Understand foundational data structures and the principles of tidy data; Acquire raw data from web APIs, databases, and common file formats; Identify and handle missing values, duplicates, and formatting inconsistencies; Reshape and merge datasets using modern dataframe libraries like pandas; Apply regular expressions to parse and clean unstructured text data; Validate data quality and automate basic cleaning workflows. You will start with core definitions and structural concepts before moving into hands-on reading materials that demonstrate step-by-step cleaning techniques. Through practical written examples, you will build a solid workflow for preparing data from scratch. This course is designed for beginners who want to build a strong foundation in data preparation. No prior experience with data cleaning is required, though a basic familiarity with Python is helpful. Start mastering the art of data preparation and turn raw, messy datasets into valuable insights today.